abidlabs / mnist-from-scratch-1min

MNIST from scratch — 1-minute training run

A small CNN trained from random initialization (no pretrained weights) on ylecun/mnist for ~1 minute of GPU time .

Results

Training recipe

The training loop was wall-clock bounded ( TRAIN_SECONDS=55 ) with the shuffled DataLoader cycled, so the run ends on the clock rather than after a fixed number of epochs. An earlier version of the script stopped after one epoch (938 steps, 10.7 s, 0.9801 test accuracy); the weights here are from the full-55-second run.

Usage

import torch
import torch.nn as nn
from huggingface_hub import hf_hub_download
import numpy as np

class Net(nn.Module):
    def __init__(self):
        super().__init__()
        self.conv = nn.Sequential(
            nn.Conv2d(1, 16, 3, padding=1), nn.ReLU(), nn.MaxPool2d(2),
            nn.Conv2d(16, 32, 3, padding=1), nn.ReLU(), nn.MaxPool2d(2),
        )
        self.head = nn.Sequential(
            nn.Flatten(), nn.Linear(32 * 7 * 7, 128), nn.ReLU(), nn.Linear(128, 10),
        )

    def forward(self, x):
        return self.head(self.conv(x))

model = Net()
path = hf_hub_download("abidlabs/mnist-from-scratch-1min", "pytorch_model.bin")
model.load_state_dict(torch.load(path, map_location="cpu"))
model.eval()

# x: float32 tensor of shape (N, 1, 28, 28), pixel values in [0, 1]
# logits = model(x)

Run metadata (steps, timing, accuracy, seed) is in training_meta.json .

Links

• Metrics: https://huggingface.co/spaces/abidlabs/mnist-from-scratch-1min-trackio
• Dataset: https://huggingface.co/datasets/ylecun/mnist